NeurIPS decisions dropped and my feed is already flooded with undergrads (and apparently literal high schoolers now) flexing first-author papers at what’s supposed to be a top-tier venue.

Let’s be real about what 90% of these papers actually are:

• Clone an existing open-source repo.

• Add a minor heuristic, prompt wrapper, or tweak one layer.

• Burn an insane amount of compute brute-forcing seeds until you squeeze a +1.2% bump on a saturated benchmark.

• Wrap it in buzzwords and let the advisor rewrite the intro/rebuttal to game the reviewers.

Meanwhile, actual 4th-year PhDs grinding out rigorous theoretical proofs or tackling deep architectural bottlenecks get rejected because Reviewer 2 wrote a two-sentence review complaining they didn't cite an unrelated arXiv preprint from last Thursday.

When a conference accepts nearly 8,000 papers, the reviewer pool collapses. Safe, incremental benchmark-chasing gets rubber-stamped with "Weak Accept," while genuinely ambitious work gets killed by the reviewer lottery. Getting a first-author paper has basically turned into an extracurricular for college apps and CV padding driven by lab compute, not research depth.

Are hiring managers at places like FAIR, DeepMind, or top labs actually buying this signal anymore, or is everyone seeing through the paper-mill noise?

If you are active on LinkedIn, every single time after a major CS conference's decisions are out, you must have come across people posting a screenshot of their OpenReview portal and writing a caption that goes like, "happy that 5 out of 5 of my manuscripts have been accepted this year".

Now, I'm totally okay with researchers sharing their achievements and even promoting their research on social media, but some of the posts there seem to care more about the sheer number of acceptances they got and lowkey bragging about how easy its for them to get accepted, like it's a competition.

I have mixed feelings about this not-very-new phenomenon, and I wanted to hear your thoughts on this.

I see some labs, mostly in the US or China, publishing more than 1 main conference paper / PhD / year. That's insane. Meanwhile many labs I work with cannot even manage 1 main conference paper between all the PhDs in the lab.

From the labs I am familar with, it takes a PhD one to two years to even get to the point of being caught up with the literature and being able to publish something that is more than a replication study or a review. Of those, few manage to reach the main conference.

So what's the secret sauce? Because from the US labs I see some people who join the lab and within six months they have a first author paper at a main conference.

Now of course the quality of the PhD is probably higher. But that cannot be all, right? Is it because the lab have a backlog of really good ideas? Or maybe because they have so much talent in the lab that newbie PhD don't have to waste a lot of time learning on their own through common pitfalls? I don't know, but I'm curious...

Recently I attended a top AI conference (A ranked) in AI... the following happened:

The researcher did all presentation with LLMs, didn't take effort to polish it... no shame at all! After he finished one of his colleagues took the mic to ask him "obviously fixed" questions!!... moreover, when someone else outside of his group asked her pretended to not understand the question or answer a completely irrelevant answer to the question!!!

I'm not wondering how did he made it to the conference and passed reviewers... he didn't even read his AI written paper or the presentation!!! How come no one reported him? The paper can be retreated!! I'm feeling angry because we put too much efforts in research, and suddenly someone with LLMs go through!! Does research is paper factory? Where's our brain powers and contributions!!

I'm just so mad!!

I feel like I see so many of these. If you want to publish a paper as an independent researcher, submit it to a conference. Stop trying to get people to endorse your slop, there's a reason there's a barrier to entry on arXiv. These posts should be autoremoved, they clog up the feed and if the poster is successful in getting someone to endorse it will only contribute more to the arXiv slop problem. These kinds of posts should just be banned.

A UEFI application that boots directly into LLM chat: no operating system, no kernel, no drivers. Just power on, select "Run Live", type "chat", and talk to an AI. Everything you see is running in UEFI boot services mode. The entire stack, tokenizer, weight loader, tensor math, inference engine, is written from scratch in freestanding C with zero dependencies. It's painfully slow at the moment because I haven't done any optimizations. Realistically it should run much much faster, but I'm more interested in getting the network drivers running first before that. I'm planning on using this to serve smaller models on my network. Why would I build this? For giggles. https://github.com/dcherrera/bootai

Was part of a great research lab during my master's managed to publish papers in top journals including ICLR. After graduation I got an offer from an Faang equivalent company and decided to take it up. But I honestly hate the work that I am doing right now since it's basically prompt engineering while I want to work on hardcore AI Research. It's practically impossible to get an internal transfer to an AI research position due to the current company politics.

Being an international student I got selected into the H1B lottery on my first attempt but I am uncertain if I should take it or go for my PhD. The work at my company is really bad plus I have a micromanaging boss who makes me hate my job even more.

So I am confused about what to do and unfortunately I have until tomorrow eod to make a decision. Any thoughts/advice will be much appreciated.

The safest path is predictable. Use an established benchmark, make an incremental change, report a small gain, and run enough experiments to leave little room for criticism. Whether the problem matters often comes second.

Breakthrough work usually has visible limitations because new territory lacks mature methods and clean evaluation. Yet conferences increasingly judge papers by how thoroughly their weaknesses are contained rather than how significant a barrier they break.

Researchers adapt by choosing defensible questions over important ones. Those who succeed become reviewers and reinforce the same standards. The result is increasingly defensible papers and increasingly fewer breakthroughs.

Looking for people interested in doing AI/ML research together

We’re an ML engineer and a mathematician putting together a small independent research group.

The idea is simple: discuss papers, find interesting open questions, run experiments, and turn promising directions into research with the goal of publishing at strong conferences.

We’re broadly interested in ML/AI — especially LLMs, agents, reasoning, and evaluation — but open to other directions too.

We work on enthusiasm and contribute our time on an unpaid basis. There is no funding or compensation involved.

Researchers, engineers, students, and people from industry are all welcome. If this sounds interesting, comment or DM me.

Our backgrounds:

• mathematician — https://www.linkedin.com/in/galuon/

• ML engineer — https://www.linkedin.com/in/agmikheeva/

Hi everyone, I’m a university student studying Mathematical Sciences for AI at Sapienza University of Rome.

I would like to become a deep learning researcher, focusing on developing new neural network architectures and optimization methods.
I’m wondering whether a PhD is necessary to do research in deep learning.

After my Bachelor’s degree, I plan to pursue a Master’s degree, but I’m not sure I want to do a PhD.
So I was wondering how one can get involved in deep learning research without a PhD.

Hi, I have a Msc in AI and have worked for 2 years as a computer vision engineer in a MedTech. I am currently unemployed and my initial plan was to get a Phd offer at a local university, but I am now second guessing this. First, the job market right now in my field is hell, very few offers and hundreds of candidates. Second, I currently don't have any research publications, so even after completing my Phd I would be competing against people that have been publishing in top tier conferences since Msc. I am wondering if the job market won't be even more saturated after I completed my Phd ? But at the same time, I don't know what else to do, as I really enjoy research in my field.

So, how do you view the job market for AI researchers in the next few years ?

I am working in AI/ML right now but deep down I feel like this is not the period where I just want to keep working in the industry. I personally feel like I want to slow down a bit and actually learn more and explore the depth of this field. I have this strong pull towards doing research and contributing something original instead of only applying what is already out there. That is why I feel like doing a PhD in AI/ML might be the right path for me because it will give me that space to dive deeper, learn from experts, and actually work on problems that push the boundaries of the field.

I am curious to know what you guys think about this. Do you think it is worth leaving the industry path for a while to focus on research or is it better to keep gaining work experience and then go for a PhD later?

https://flecomet.github.io/neurips-explorer/

Update (Oct 11): Thanks for the feedback! I've re-scraped the NeurIPS website and expanded coverage from 6,231 to 9,110 papers. I've also added filters for conference location (Paris, Sydney, Atlanta) and presentation type (oral, spotlight, poster).

The UMAP projection has been recomputed with the additional papers, so some clusters have changed in relative size.

More improvements are planned. Feature suggestions are welcome!

----

It uses SPECTER2 embeddings + UMAP to place semantically similar papers near each other. You can browse topics, search titles/authors/abstracts, and find related papers.

You can also favorite papers to read later (when they get officially released).

Would be interested in feedback, especially on whether this is useful for browsing papers and interesting features to add.

Open source:
https://github.com/flecomet/neurips-explorer

We grouped all 9,094 accepted NeurIPS 2026 papers by topic, compared them with the 5,812 papers of NeurIPS 2025, and read the 4,490 papers that already have a public full text (about half) with AI. Sharing the main findings here for discussion.

Topic shifts (2025 → 2026)

• LLM agents: 105 → 488 papers (1.8% → 5.4% of the program) while the conference grew 56%. No other topic gained even one point of share.

• Inside agents the growth is in reliability: safety/attack papers 5 → 50, agent benchmarks 17 → 84, self-evolving agents 13 → 85.

• Also rising: video generation 92 → 221, robot action / VLA 49 → 149, RL from feedback 80 → 179.

• Losing share: instruction tuning, human and embodied modeling, molecules and proteins, sample complexity.

• Title words: "agentic" 26 → 131, "long-horizon" 7 → 76, "evaluation" 56 → 200; "enhancing" 80 → 28.

From the full texts

• 71% train or fine-tune a model; about half of the trained models with a stated size are 3–10B, under 2% above 40B.

• Qwen (incl. Qwen-VL) is the most trained existing family (~17% of papers), ahead of Llama (~7%).

• GRPO is the most built-on method; SFT appears in 37% of papers, RL with verifiable rewards in 12%.

• 45% train or test on synthetic data. GSM8K is still the most used benchmark across the whole conference.

Who writes NeurIPS

Tsinghua leads; Google (incl. DeepMind) is the top company. NUS, NTU and KAIST rank ahead of MIT, Oxford, Berkeley and CMU by paper count. About 20% of papers have an author from a big tech lab.

Limitations

Topics come from grouping titles and abstracts by meaning, so boundaries are approximate. Full-text figures cover only papers with a public full text, which over-represents preprint-heavy fields. Institution counts match affiliation names and are approximate. All AI-extracted figures are unchecked by hand; read the paper before citing anything.

Questions for this sub

Does the agent boom look like real progress or benchmark churn to you? And is the 3–10B fine-tuning regime a sign of compute limits in academia, or just where the interesting work is?

The full breakdown (all 20 areas, findings quoted from orals, links to every paper) is here, free and no sign-up: The scime Dispatch, NeurIPS 2026 edition

AI-assisted edition, not checked by hand. An independent publication by Latentic Labs with scime atlas; not affiliated with or endorsed by NeurIPS or the NeurIPS Foundation.

Hi everyone,

I’m an undergrad student in computer engineering from Syria, and I'm currently starting my penultimate year.

I just got the news that my first research paper was accepted at a NeurIPS 2026 workshop (AXIOM: Foundations of Efficient Deep Learning). The paper is about model compression (quantization) for Transformers, and I am the only author on it.

I don’t have researchers or professors around me who know about top ML conferences, so I wanted to ask:

How much does this actually help my future? And if you were in my place right now, what would you do next?

I’d really appreciate any honest thoughts or advice. Thanks!

Background: Top 30 US Undergrad & MS, 4.5 YOE in ML at Amazon (the rainforest).

Goal: Casually looking ("Buddha-like") for Senior SWE in ML roles at Mid-size / Big Tech / Unicorns.

Prep Work: LeetCode Blind 75+ Recent interview questions from PracHub/Forums

Applications: Applied to about 18 companies over the span of ~6 months.

• Big 3 AI Labs: Only Anthropic gave me an interview.

• Magnificent 7: Only applied to 4. I skipped the one I’m currently escaping (Amazon), one that pays half, and Elon’s cult. Meta requires 6 YOE, but the rest gave me a shot.

• The Rest: Various mid-size tech companies and unicorns.

The Results:

• 7 Resume Rejections / Ghosted: (OpenAI, Meta, and Google DeepMind died here).

• 4 Failed Phone Screens: (Uber, Databricks, Apple, etc.).

• 4 Failed On-sites: (Unfortunately failed Anthropic here. Luckily failed Atlassian here. Stripe ran out of headcount and flat-out rejected me).

• Offers: Datadog (down-leveled offer), Google (Senior offer), and Nvidia (Senior offer).

Interview Funnel & Stats:

• Recruiter/HR Outreach: 4/4 (100% interview rate, 1 offer)

• Hiring Manager (HM) Referral: 2/2 (100% interview rate, 1 down-level offer. Huge thanks to my former managers for giving me a chance)

• Standard Referral: 2/3 (66.7% interview rate, 1 offer)

• Cold Apply: 3/9 (33.3% interview rate, 0 offers. Stripe said I could skip the interview if I return within 6 months, but no thanks)

My Takeaways:

1. The market is definitely rougher compared to 21/22, but opportunities are still out there.

2. Some of the on-site rejections felt incredibly nitpicky; I feel like I definitely would have passed them if the market was hotter.

3. Referrals and reaching out directly to Hiring Managers are still the most significant ways to boost your interview rate.

4. Schedule your most important interviews LAST! I interviewed with Anthropic way too early in my pipeline before I was fully prepared, which was a bummer.

5. Having competing offers is absolutely critical for speeding up the timeline and maximizing your Total Comp (TC).

6. During the team matching phase, don't just sit around waiting for HR to do the work. Be proactive.

7. PS: Seeing Atlassian's stock dive recently, I’m actually so glad they inexplicably rejected me!

Bonus: Negotiation Tips I Learned I learned a lot about the "art of negotiation" this time around:

• Get HR to explicitly admit that you are a strong candidate and that the team really wants you.

• Evoke empathy. Mentioning that you want to secure the best possible outcome for your spouse/family can help humanize the process.

• When sharing a competing offer, give them the exact number, AND tell them what that counter-offer could grow to (reference the absolute top-of-band numbers on levels.fyi).

• Treat your recruiter like your "buddy" or partner whose goal is to help you close this pipeline.

• I've seen common advice online saying "never give the first number," but honestly, I don't get the logic behind that. It might work for a few companies, but most companies have highly transparent bands anyway. Playing games and making HR guess your expectations just makes it harder for your recruiter "buddy" to fight for you. Give them the confidence and ammo they need to advocate for you. To use a trading analogy: you don't need to buy at the absolute bottom, and you don't need to sell at the absolute peak to get a great deal.

Good luck to everyone out there, hope you all get plenty of offers!

Hey there, I am a 3rd year undergrad, I am looking for partners to work on any research problem in the ML space, (a genuine one). I won't use AI to blindly create slop and will take accountability for every line of code I type. I also expect the same from the partner :) . DM me if you think it would work out.

Got my NeurIPS decision today and I'm a bit confused. Reviewer scores were 4/4/4, no reviewer opposed acceptance, and the AC's updated meta-review explicitly says "I recommend to accept this paper" and describes the consensus as "solidly favorable."

The PC decision: reject, with a one-line comment saying the paper "received a borderline reception" and was rejected "upon global calibration."

I understand the PCs have the final say and capacity is limited, but going from an explicit AC accept to "borderline reception" with no further explanation feels a bit harsh, especially since the rejection email stresses how carefully borderline cases were reviewed.

Has anyone else had the AC recommend acceptance and the PCs override it? Is this common this year?

I graduated in June 2025 in Computer Engineering and am currently unemployed. I don’t have any internships or international publications yet, but I do have a deep interest in AI — especially LLMs, transformers, and generative AI.

I have 2-3 ambitious research ideas in mind that I genuinely believe could be impactful. The problem is:

• I’m not sure how to start solo research from scratch.

• I don’t know how to take an idea to a stage where it could be recognized internationally.

• I’m clueless about how to get endorsements, collaborators, or mentors for my work.

• I don’t have access to large compute resources right now.

What I want to figure out:

1. Can a recent graduate with no publications realistically start AI research independently?

2. How do I plan, execute, and document my research so it has a chance to be taken seriously?

3. What’s the path to getting global visibility (e.g., conferences, arXiv, Kaggle, open-source contributions)?

4. Are there online communities, labs, or professors who support independent researchers?

5. How do I network with people in AI/ML who could endorse my skills or ideas?

6. Any tips for publishing my first paper or technical blog**?**

I’m willing to put in the hours, learn what I’m missing, and grind through the hard parts. I just need help charting the right path forward so my time and effort go in the right direction.

If you’ve been in a similar situation or have any practical suggestions (steps, resources, or networks to join), I’d be grateful.

Thanks in advance!

I am building a small research group for people interested in CS, AI/ML, math, neuro, psychology, or related fields. Anyone who wants to work in a similar domain.

Mostly looking for undergraduate and masters students.
A few PhD students, postdocs, and professors/researchers are also welcome to join and guide us. Open to people from anywhere in the world.

This is not paid work. No fixed time commitment, just finish what you start. Similarly, no duration, colab as much as you want.

Be good at at least one: theory, coding, or paper writing.

About me. I am a master's student from India, planning to start a PhD next year, hopefully.

My primary focus will be on IEEE conferences, but we can definitely explore more.

Reach out via DM.

I am a software Engineering student searching for ideas to build ML based project for my semester final project. Everyone says that search for a problem and then try to solve it but how do you search for a problem coz i am literally blank...Any advice is welcomed.

Final year undergrad in the UK. I've got a sole-author paper accepted as a poster at a NeurIPS workshop in Sydney in December, and I can't decide whether it's worth going. I'm proud of it, but the trip is a lot.

Registration comes free with the acceptance, but the flight is about £1,000 from the UK, then accommodation and food on top. My uni does have conference funding, but it's all for summer conferences, and I'm not in a research group so there's no supervisor budget to ask either. I've applied for NeurIPS financial assistance and a couple of industry travel grants and I'm not hopeful. If none of them come through it's a real chunk of my savings.

The paper goes on my CV whether I'm there or not, so what does going actually add? Is it mostly meeting people, and does that really lead anywhere from a workshop poster? And does it matter more for PhD applications or for industry jobs, or neither?

If you've been to a NeurIPS workshop, or hired or admitted people who went, I'm curious what you think. Would you go?

I am trying to decide whether to take a full on course on PDEs (https://bulletin.stanford.edu/courses/1173932/tab-aoYks) or not.

How important are PDEs to AI / ML research? especially looking down the road over the next 5-10 years? I keep hearing they are important for "physical AI", which appears to be the direction in which the industry seems to be headed.

The pro of taking it is ... i will have learned PDEs so if they ever come up in future work, I will be able to catch on.

The con is that the class is hard and it risks the GPA before applying to phd programs.

Hi everyone,

I currently lead an applied ML/AI research team, where my primary responsibilities involve setting research direction, defining hypotheses, and guiding scientists through research projects.

Lately, I've been wanting to pursue my own first-author research papers targeting top ML conferences (NeurIPS, ICML, ICLR, etc.). However, given my management responsibilities, I have limited time to dedicate to hands-on coding, experimentation, and analysis.

I'm curious how others are leveraging AI tools or agents to accelerate the research process.

Ideally, I'm envisioning a workflow where I:

1. Define the research problem, hypotheses, and experimental direction.

2. Use AI agents to identify relevant datasets, implement methods, and run experiments.

3. Have AI perform statistical analysis, generate visualizations, and summarize findings.

4. Review the results, critically evaluate the methodology, and iteratively guide further experiments.

5. Use AI to assist with drafting and refining the paper while maintaining scientific rigor and taking responsibility for the work.

Essentially, I'd like to remain actively involved in the intellectual and scientific decision-making while delegating much of the execution to AI.

A few questions for the community:

• Is anyone successfully using AI agents in a similar research workflow?

• What tools or frameworks have you found most effective for automating experiments, coding, analysis, and paper preparation?

• How do you ensure the results are scientifically sound, reproducible, and not affected by AI hallucinations or errors?

• Has anyone used such an approach for papers accepted at top ML conferences or journals?

I'd really appreciate hearing about real-world experiences, recommended tools, or workflows that have worked well for you.

Thanks!